Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings

TL;DR

This paper introduces a semi-supervised method for ontology population using word embeddings, combining multiple models into an ensemble to improve accuracy in concept hierarchy expansion.

Contribution

The study presents a novel ensemble approach leveraging word embeddings for semi-supervised ontology population, outperforming individual models.

Findings

Ensemble model achieves higher accuracy than individual models.

Word embeddings effectively capture semantic relationships for ontology population.

Semi-supervised approach reduces manual intervention in ontology expansion.

Abstract

In many modern day systems such as information extraction and knowledge management agents, ontologies play a vital role in maintaining the concept hierarchies of the selected domain. However, ontology population has become a problematic process due to its nature of heavy coupling with manual human intervention. With the use of word embeddings in the field of natural language processing, it became a popular topic due to its ability to cope up with semantic sensitivity. Hence, in this study, we propose a novel way of semi-supervised ontology population through word embeddings as the basis. We built several models including traditional benchmark models and new types of models which are based on word embeddings. Finally, we ensemble them together to come up with a synergistic model with better accuracy. We demonstrate that our ensemble model can outperform the individual models.